A ForestPaths paper by project partner VITO, together with RWTH Aachen University, Paul Scherrer Institute and Leiden University, proposes a major step forward in integrating time into Life Cycle Assessment (LCA). The study introduces a framework that makes LCAs explicitly reflect when processes happen and how technologies change over time. 

Traditional LCAs assume static conditions, typically ignoring how processes, emissions and environmental impacts unfold over time. Existing advanced approaches either consider the timing of emissions (dynamic LCA) or future technological change (prospective LCA), but rarely both. The new time-explicit LCA framework fills this gap by jointly modelling when processes occur and how technologies and supply chains evolve over time, offering more realistic assessments of variable or long-lived systems. 

The method first determines the temporal sequence of supply chain processes based on user input. It then expands the conventional LCA matrices to include time-specific processes, and connects them to prospective background data through temporal markets. This produces temporally resolved emission data reflects the status of technology at each point in time and can be characterized with either conventional impact assessment methods or dynamic methods. The latter also consider the impact of emission timing on environmental responses. 

The framework is implemented in the open-source Python package bw_timex, part of the Brightway ecosystem. Using a simplified electric-vehicle case study, the authors show how time-explicit LCA can produce more representative results than static or purely prospective LCAs, while remaining fully compatible with established LCA data structures and workflows. 

While challenges remain, e.g. data availability and computational demand, time-explicit LCA marks a significant step toward easy-to-apply temporally nuanced sustainability assessments, with strong potential for integration with dynamic Material Flow Analysis or spatial LCA.  

Read the full study here.